E3 Strategy Reduces LLM Agent Costs by 85% While Maintaining Task Success

Short summary

Researchers propose E3 (Estimate, Execute, Expand), a strategy to stop LLM agents from over-reading context by having them first estimate task scope, execute a minimum viable path, and only expand on failure. On MSE-Bench, E3 matched baseline success rates while cutting costs by 85%, tokens by 91%, and inspected files by 92%. A companion test with GPT-4o on a real open-source library confirmed the gains, positioning task-aware execution as foundational for efficient engineering AI agents.

• •E3 strategy makes LLM agents estimate scope before acting, expanding only on failure
• •Benchmarked on MSE-Bench: 85% cost reduction, 91% token reduction, 92% fewer files inspected vs baseline
• •GPT-4o agent tests on real open-source library corroborated efficiency gains

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